Alessandro Pavan (Northwstern University)
"Robust predictions in dynamic screening"
We characterize properties of optimal dynamic mechanisms using a variational approach thatpermits us to tackle directly the full program. This allows us to make predictions for a considerably broader class of stochastic processes than can be handled by the first order, Myersonian,approach", which focuses on local incentive compatibility constraints and has become standardin the literature. Among other things, we characterize the dynamics of optimal allocations whenthe agent's type evolves according to a stationary Markov processes, and show that, provided theplayers are sufficiently patient, optimal allocations converge to the efficient ones in the long run.